Working Paper Series

Department of Economics

University of Verona

Socio-Economic Determinants of Student Mobility andInequality of Access to Higher Education in Italy

Umut Turk

WP Number: 8 May 2017

ISSN: 2036-2919 (paper), 2036-4679 (online)

Socio-Economic Determinants of Student

Mobility and Inequality of Access to Higher

Education in Italy∗

Umut TURK †

Abstract

This paper introduces a modified version of the Hansen-gravity model as a

framework to estimate the accessibility of higher education (HE) institutions

in Italy from equal opportunities perspective. The key assumption underly-

ing gravity models is that accessibility decreases with spatial distance from

opportunities. The paper extends the gravity equation to allow for the inclu-

sion of socio-economic factors influencing the access to HE. The findings reveal

differences in response to quality and to other institutional characteristics by

parental background and gender. Finally, decomposition of overall inequal-

ity into spatial and aspatial components reveals both the physical and social

distance between groups of students seeking higher education opportunities in

the country.

Keywords Spatial Interaction, Higher Education Accessibility, Gravity Model,

Equality of Opportunity

∗This paper has benefited from the insightful comments and suggestions of the partici-

pants of 9th GRASS Workshop in Lucca, Italy; RSAI/ERSA Workshop on Regional Science

and Urban Economics Spatial Perspective of Human Capital in Barcelona, Spain; Joint

meeting NECTAR Cluster 6 Accessibility/Regional Science Academy in Paris, France.†Department of Economics, University of Verona, Verona, ITALY. Email address:

umut.turk@univr.it

1

1 Introduction

An intuitive way to increase spatial accessibility is to decentralize the service

in question. This was the strategy implemented by the Italian authorities in

the period 1990-1998. With one of the lowest participation and graduation

rates in Europe, the supply of higher education (HE) was expanded drasti-

cally. The reforms required larger universities to set up new types of faculties

and 9 new higher education institutions were established as a result of the

decentralization process (MIUR). However these reforms took place without

any field examination of accessibility or demand (Bratti et al., 2008). More

than a decade later, there is no explicit measure of spatial accessibility of the

universities in the country.

In a system granting free access to HE for every potential candidate,

the foremost aim of policy makers is to guarantee full accessibility to the

service irrespective of the location of residence. Previous research has focused

on measuring accessibility through an examination of the match between the

locational distribution of facilities or services and the locational distribution

of residents (Talen & Anselin, 1998). In this framework, the spatial distance

between residence of origin and the location where opportunities are located

is regarded as an important factor determining the spatial accessibility. The

underlying idea is that people from more isolated locations face larger costs to

access to opportunities, with costs growing with spatial distance.

Since the residential location of students prior to HE enrolment is

detemined by parents, inequalities in access to HE across students’ locations

of origin should be regarded as unfair. Modern theory of inequality, building

on equality of opportunity (EOp) arguments, suggests that the differences in

outcomes due to factors that are beyond individual responsibility are unfair

and should be compensated by society (Dardanoni et al., 2006). Reducing

geographical disparities in accessibility can be seen, in fact, as a way of leveling

the playing field (Roemer, 1998) and providing equal opportunities to benefit

2

from HE irrespective of the place of origin. The geographical location can be

an unfair source of accessibility on the large scale: a student living in an urban

area where HE services are supplied faces smaller costs of transportation, lower

opportunity costs in commuting and no housing costs, compared to students

living in the countryside who need to commute or move to benefit from HE

services. However, focusing only on geography may leave the influence of

socio-economic factors, in relation to gender, experiences at home and parental

background, unexplored.

The paper argues the existence of a gradient of economic circum-

stances of origin on distance elasticity. Although distance matters in explain-

ing accessibility, there are other variables that determine differences in costs of

movement, correlated with distance which, at the same time, might influence

the distribution of accessibility. This paper tries to single out the contribution

of spatial distance and economic circumstances on inequality in accessibility

to HE by using a multidisciplinary approach, where the problem of disparities

in spatial access is redefined on the basis of both the physical distance from

universities and the social distance between student groups that generates an

additional inequality in access within the same location. The paper looks at

the variability in access both when focusing on comparisons of people located

at different origin points from HE, but all sharing the same family background

(highlighting the share of inequality due to spatial distribution of HE institu-

tions in the country) and when comparing people located at the same origin

points but with differing backgrounds of family origin, which is taken as a

proxy of the ability of families to cover costs of displacement and, if possible,

to compensate for distance from the location of origin. The latter shows the

share of inequality in access due to the socio-economic background of students.

In an empirical application, the paper sequentially employs a model

and an index to measure overall inequality in access, which is then decom-

posed into its geographical and socio-economic components ; first a spatial

interaction model (SIM) is used to disentangle the migration dynamics of dif-

3

ferent student groups. Being flexible and simple enough, these models enable

the investigation flows between origins and destinations (Sen & Smith, 2012).

Actual flows of commodities, information, emails, phone calls, money and of

people along with any other sort of movements are likewise applicable to SIMs

(see Haynes & Fotheringham, 1984; Sen & Smith, 2012, for reviews). In the

present application, student flows from parental residents to universities are

defined as interactions between localities and, to account for socio-economic

factors in place, the total observed flows of students are partitioned into sub-

groups each representing a different type. It is a common practice for EOp

studies to partition the population according to exogenous factors, which are

assumed to be beyond people’s control (see for instance Checchi & Peragine,

2010; Ferreira & Gignoux, 2011) and resulting subgroups are defined as types

(Roemer, 1998). For the second step, the parameters that are distinctively

calibrated for each type by the SIMs are imported to a Hansen (1959)-like

index to measure potential accessibility for 110 Italian provinces (NUTS3 level

regions). Finally the inequality in accessibility among provinces is decomposed

as follows: the access score in each province is replaced with its average access

score across socio-economic groups hence only variation is allowed to be due to

the geographical distribution of universities. Then the access scores computed

for each socio-economic group is replaced with its average access score across

provinces hence remaining variation is allowed to be due to socio-economic

backgrounds. This operation enables investigating the relative contributions

of spatial and aspatial factors.

The paper contributes to the literature by extending classical spa-

tial accessibility analysis to incorporate the socio-economic circumstances of

students in a spatial accessibility measure for Italian HE institutions. This

practice goes beyond the mere concern of inequalities on outcomes. For the

spatial accessibility analysis this means that the inquiry may shift from ”spa-

tial accessibility where?” to ”spatial accessibility where and for whom?”. It

also contributes to the EOp literature by showing how the spatial dimensions

4

of the theory can be incorporated into models that rely solely on geography.

Finally, the findings in this paper provide highly detailed information for policy

makers regarding which groups of students to target and specifically in which

locations the assistance is needed most.

The remainder of the paper is organized as follows: the second section

introduces the model and the accessibility index adopted, the third section sets

out the data and variables, the fourth section shows empirical method for cali-

bration and findings where inequality in access is decomposed into within and

between components. Finally the conclusions and possible policy implications

are given in the fifth section.

2 Theoretical Framework

This section presents the model adopted for student flows and the potential ac-

cessibility index. The link between these two builds on the distance parameter

assumed to reflect both physical and social costs in migrating or commuting to

destination universities. The response to distance is expected to be conditional

on the socio-economic background of students.

2.1 A Spatial Interaction Model of Student Mobility

Spatial interaction models are used to predict the size of spatial flows between

origins and destinations in areas of interest. They have been used mainly

for transportation and environmental planning, then developed further for a

variety of applications where a movement and/or interaction takes place. Re-

cently, most applications relate to health system planning, decisions concerning

hospital locations, the analysis of interaction between patients and physicians

as well as labour studies such as job accessibility and an investigation of the

daily commute to work (Mayhew et al., 1986; R. M. Wilson & Gibberd, 1990;

5

Reggiani et al., 2011).

Particularly with regard to SIMs applied to HE choice, Sa et al. (2004)

studied the demand for HE in the Netherlands, given the attractiveness and

accessibility of universities. Alm & Winters (2009) correlated the distance

from parental residence to state HE institutions with tuition, financial aid and

school quality as institution fixed characteristics and found a varying deter-

rence effect of distance in relation to these characteristics. Cooke & Boyle

(2011) included several origin and destination attributes to SIMs, including

the number of high school graduates in origins, employment growth both in

origins and destinations and the relative quality of amenities. Singleton et

al. (2012) integrated SIMs with geodemographic analysis, and looked at both

socio-spatial conditions in the neighbourhood and the attractiveness of des-

tinations. For the Italian data, Dotti et al. (2013) investigated the role of

universities in attracting successful students to certain regions and to settle

down there after graduation. These studies estimate the distance elasticity

of university choice given the attributes of origins/destinations and some also

include university characteristics. However they do not incorporate the socio-

economic profile of students in the analysis. The present paper examines the

role of socio-economic characteristics of students in distance elasticity. Fur-

thermore, based on the distance elasticity values, the paper transforms SIMs

into an explicit measure of accessibility.

SIMs can incorporate a range of origin and destination constraints

and take a number of forms according to this constraint structure. The fol-

lowing formula is a production-constrained form of SIMs that suggests that

the interaction between any two units must be directly proportional to the

masses of origin and destination and inversely related to the distance between

them. The basic assumption is that a positive interaction between each pair

of locations exists 1.

1(see A. S. Fotheringham & Webber, 1980; A. Fotheringham & O’Kelly, 1989; Sen &

Smith, 2012, for reviews)

6

Tij = KiOθiD

αj f(dij) (1)

Tij refers to student flow from their residence i to university j

Oi origin dummies for 110 Italian provinces (NUTS3 level regions)

Dj total number of students reaching university j

Ki = [∑

j Dαj f(dij)]

−1 is the balancing factor ensuring that the marginal

total constraint∑

j Tij = Oi is satisfied.2

f(dij) = d−βij where dij is the Euclidean distance between city i and

university j

For this application, the model is extended to include several univer-

sity fixed characteristics and interactions with distance. Finally the following

model is obtained:

Tij = KiOθiD

αj Sj

γLjη exp(−βln(dij) + µδij +

∑

l

λlln(dij)Ujl) (2)

where Sj and Lj are two variables accounting for the attractiveness of a des-

tination. Following the previous studies (see for example Lowe & Sen, 1996;

Gitlesen & Thorsen, 2000; McArthur et al., 2011) a Kronecker delta is added

to the model as follows:

δij =

1 i = j

0 otherwise

2 With Ki the model becomes production-constrained. The choice of this model is jus-

tified by the fact that most of the programmes are provided in an open-access fashion in

Italy. Therefore, theoretically, students are free to choose any destination desired hence the

model is not constained by destination (not attraction constrained) but to make sure that

the number of trips produced by an origin do not exceed the number of residents, the model

is constrained from production side. For the formal development see A. G. Wilson (1971)

7

The common interpretation of µ is that it reflects the benefit of residing and

studying in the same city, or a start-up cost in case i and j are not in the same

province. Furthermore f(dij) interacts with several destination characteristics

Ujl where l is the number of interaction terms and λl is the distance elasticities

given the institutional characteristics.

2.2 Adopted Accessibility Index

In this paper, the accessibility concept is interpreted as the potential availabil-

ity of HE given the spatial distribution of institutions in the country. The roots

of the index go back to Hansen (1959) when he first proposed the following

gravity model of accessibility:

Ai =J∑

j=1

Sjd−βij

where Ai is a measure of accessibility , Sj is the number of opportunities at

the destination and dij is the distance between an origin and a destination. A

similar accessibility index can be constructed as follows:

Ai =∑

j=1

Cjdβij

δij(3)

where

δij =

exp(µ) i 6= j

1 otherwise

µ and β are the two parameters that channel (2) to (3) and are

calibrated beforehand by the production-constrained SIM (2). Cj is the total

number of places offered by each institution. Additionally, the index discounts

accessibility when i and j are not located in the same province by δij.

8

3 Data and Variables

Table 1 shows the variables used in the analyses carried out in this paper. The

data is extracted from a data survey (Inserimento professionale dei laureati,

2011) including 14,000 male and 17,400 female graduates in 2007 and data from

MIUR (Ministry of Education, 2003-2004-2005). The survey data includes the

information of student residence in 110 Italian provinces (NUTS3 level regions)

before enrolling to a university and the name of university enrolled. The actual

flow of students between the province of residence and the exact addresses of

universities is extracted and stacked into a table as a column vector as the

variable of interest.

3.1 Types

This paper argues that at least three aspatial factors are particularly relevant

to the study of HE accessibility. Firstly, the role of parental education is a well-

explored factor that affects the educational choices and outcomes of students.

Specifically in the Italian context, the educational level of parents is found to

be highly influential for the academic attainment of Italian students (Checchi

et al., 2003; Bratti et al., 2008). Higher HE participation rates and less drop-

outs are observed for students with highly educated parents(Checchi & Flabbi,

2007; Brunori et al., 2012). Moreover, since commuting or migrating to a place

involves a cost, the financial condition of families is another aspatial factor

relevant to access (see Frenette, 2003; Lupi & Ordine, 2009). Finally, even

though education is the primary area where women have made substantial

gains and now largely out-perform men (DiPrete & Buchmann, 2006), the

question whether there are systematic differences in spatial access to education

among males and females remains an important one.

Observed flows are partitioned according to three sets of proxies re-

ferring to the socio-economic circumstances of students. Each subgroup forms

9

a type, which cannot be chosen by students. Accordingly, three circumstance

variables are employed as shown in Table 1: the presence of at least one highly

educated parent at home where the alternative is both parents with basic ed-

ucation. Here “basic education” means the 8 years of compulsory schooling

in Italy. ”High education” consists of parents with at least a bachelor’s de-

gree. In the survey 40.66% of mothers and 39.69% fathers are categorized as

basically educated , and 14.56% and 20.06% as highly educated, respectively.

Survey data contains information on parents’ professions. This information is

categorized as high and low for both fathers and mothers. Hence three groups

are constructed as both-low, both-high and one-high-one-low. The gender of

students is included in addition to the parental background. The combination

of these three categories resulted in 12 types as shown in Table 1.

3.2 Distance

The distance from parental residence to HE institutions strongly influences

the likelihood of participation and the HE outcomes of students (see for exam-

ple Gossman et al., 1967; McHugh & Morgan, 1984; Tinto, 1973; Ordovensky,

1995; Gibbons & Vignoles, 2009; Suhonen, 2014). The costs of commuting or

migrating may deter access or may impose a barrier when enrolling at a uni-

versity. For Italy, the empirical evidence confirms that geographical proximity

strongly influences the choice of university (Pigini et al., 2013). Indeed, in the

survey data, 59.28% studied in their hometown, 40.64% of students was moti-

vated by the closeness of the institution and only 9.74% by the prestige of the

university. For student mobility, distance does not only represent costs but is

also a predictor of how far students are allowed to live away from their families,

which is very relevant in Italy as it is a country characterized by strong family

ties (Alesina & Giuliano, 2010).

In this application dij is the Euclidean distance between the centroid

of city i and the exact address of university j. In the QGIS environment the

10

coordinates of city centres are matched with the coordinates of exact location

of universities and the Euclidean distance is calculated for each pair. According

to the interest of the investigation and the data behaviour it is possible to find

the exponential form, exponential square root, the log of distance or a relevant

combination of these (De Vries et al., 2009). To choose the most relevant form

of deterrence function, the predicted values are examined against observed

flows and the power specification of the distance proved most suitable for the

data 3

3.3 University Attractiveness

As far as institutonal attractiveness is concerned, previous studies of SIMs pro-

vide mixed findings. Sa et al. (2004) use university rankings as a quality indi-

cator for Dutch students, but the coefficient proves to be negative. Although

the authors explain this counterintuitive result as consumption behavior by

students in relation to HE (Sa et al., 2004), this is not entirely convincing.

Similarly Singleton et al. (2012) employ Times Good University Guide rank-

ings but set an arbitrary power of 0.5 rather than empirical derivation. Dotti

et al. (2013) construct an index identifying a province as attractive if inflows

exceed outflows, neglecting institutional attractiveness. This paper employs

two university fixed characteristics in order to account for attractiveness: the

share of successful students in the year before our sample’s enrolment and the

share of limited places provided by each university. In Italy after secondary

school, students take a national exam (Esame di Maturita ). The share of stu-

dents with the highest grades (90-100) from this test in the period 2002-2003

is included in the model (source: MIUR, 2004). Although many programmes

are offered on a free-access principle, some require entrance tests, indicating

excess demand for these programs. The proportion of limited places to the to-

3Also in previous studies the power- decay function has been found to be more suitable for

long distance interactions owing to the log-cost perception (A. S. Fotheringham & Webber,

1980; Reggiani et al., 2011).

11

tal number of places available at University is then used as a quality indicator

for the same period (source MIUR, 2004).

3.4 Interactions

Finally, several destination characteristics are interacted with distance to see

how the willingness to migrate to further destinations varies among different

types (see Gibbons & Vignoles, 2009, for a similar application to British stu-

dents).

Table 1 shows the interacted institutional characteristics as follows:

whether the university at destination is a private institution, dummies for

south, central and island locations and a dummy with value 1 for polytechnic

universities.

4 Empirical Method and Findings

The examination of the model is operated through related statistical log linear

models which were developed alongside entropy maximizing models. There

are several ways of handling spatial interaction models (see A. G. Wilson,

1971; Yun & Sen, 1994; LeSage & Pace, 2008). This study makes use of

the Poisson gravity models, 4 with the same statistical properties, producing

identical estimations to entropy maximization models (Baxter, 1982).

The Poisson gravity model takes the following form:

E(Nij) = Tij = OθiD

αj f(dij) (4)

where Nij indicates observed flows, whereas Tij is the expectation of observed

flows, treated as a random variable and assumed to have a Poisson distribution

4(see Flowerdew & Aitkin, 1982; Smith, 1987, for theoretical development)

12

(Baxter, 1982) 5.

The model is calibrated by the generalized linear model (GLM) pack-

age in R 6, where flows follow a Poisson distribution with a logarithmic link

between variables. The estimations are carried out separately for 12 subgroups.

Finally, the Poisson regression is:

Tijk = exp[constant+Oi+αDjk+µδij + βln(dij)+γSj+ηLj+∑

l

λlln(dij)Ujl]

(5)

where k = 1, 2, 3, ...., 12 represents types, Sj the share of successful students

and Lj the share of limited places at j, and Ujl a set of interactions on distance.

This regression produces an exponential value of factor for origin i and is

proportionally equivalent to the product KiOi, and is therefore equivalent to

the production-constrained model of the entropy-maximizing system.

Table 2 and Table 3 show the results of the first set of regressions

where the model has been applied to 10 groups. Groups 5 and 6 are not taken

into consideration due to the lower number of observations. As expected, dis-

tance has a very strong significantly negative effect, indicating a deterrence

impact for each group. For 1 meter decrease in the distance the expectation

number of student flows increases by factors varying form 1.499 to 1.709. The

impact is higher for female students than for male students except for those

with at least one highly educated parent. As a student’s family background

becomes more favourable in terms of the proxies specified above, the differ-

ence between male and female shrinks and ultimately female students feel less

deterred. As in previous studies, δ is significant at the 0.01 level for all groups

and positive in sign, capturing the benefit of residing and studying in the same

city. Similar values are observed for different types but with different motiva-

tions. For socially advantaged groups the parameter µ captures the fact that

these students usually live in big cities where large universities are located and

hence they do not need to migrate. On the other hand, groups 1 to 4 it may

5The probability mass function of flows is given by Pr(Tij) =exp

−Nij NTijij

Tij !6(see Dennett, 2012, for details)

13

reflect the actual startup costs where these students decide to migrate. As

far as the attractiveness of universities is concerned, Sj (the share of success-

ful students at university) is significant and positively affects flows only for

students who have at least one highly educated parent. Other students seem

to be unaffected by institutional quality. The effect is observed for Lj (the

share of limited places offered by universities) again for socially advantaged

groups. Among students with disadvantaged parental backgrounds, only fe-

male students (groups 2 and 4) are attracted to these limited positions. This

is probably due to the fact that female students are interested in faculties

such as medicine and nursing, requiring entrance tests. Hence, for students

with a poor parental background, what seems to matter is obtaining a degree

irrespective of the prestige of the University (Triventi & Trivellato, 2009).

The remaining results allow for interactions between institutional

characteristics and distance. Private universites at destination increase the

tendency of travelling longer distances for all groups. It is an expected result

since for any type deciding to enrol a private university, distance must be be-

coming irrelevant. Looking at the significance levels, polytechnics do not seem

to induce students to travel far except for groups 2 and 7. In contrast to Dotti

et al. (2013), interacting distance with macro regions, where universities are

located, shows that the central region attracts more students than the south

for all students except types 1 and 3. These two types comprise male students

with poor family backgrounds who may prefer Universities in the south due to

the lower cost of living. Finally universities located in Sicily and Sardinia fail

to attract students from all backgrounds.

Table 2-Table 3 [About Here]

After obtaining the parameter values from (5), accessibility scores are

calculated through Equation 3 for each group with their respective impedance

functions as follows:

Aik =∑

j=1

Cjdβk

ij

δijk(6)

14

where k = 1, 2, 3, ...., 12. As it for the production constrained SIM,

dij is constructed from city centroids to the exact addresses of universities (to

the largest campuses),so there is no zero distance, which in return accounts

for the self-potential (local demand) of universities within provinces. The

measured scores indicate potential access in terms of the places offered to

students. Higher scores indicate better accessibility to the 77 total number of

universities located in 101 different provinces. Maps 1-2 illustrate the access

scores of 10 groups, where darker blue indicates a higher score.

Figure 1-2 [About Here]

The first thing to note from the figures is that if a student belongs

to a socially advantaged group, then their access is relatively higher where

ever they live, except very far south in the country. Similarly for socially

disadvantaged groups, even if they live in a big city where large universities

are located, access remains low, particularly in the south. The lowest access

is observed for group 2 where the type comprising female students with lower-

class parents with a basic education. The types constructed for this paper

seem particularly relevant for female students. Access increases on average

101% from group 2 to group 12 whereas parental background does not seem

to affect male student access to HE as much as female students. From the

least to the highest, access increases 10% on average. Moreover a degree of

gender discrimination in access is observed in the first 4 groups, but lessens as

parental education and financial condition improve.

Decomposition of Access Inequality

To disentangle the relative contributions of spatial and aspatial fac-

tors to inequality, a decomposable inequality index is used 7. As shown in

Table 4, the resulting inequality is a sum of within and between inequalities.

7Mean Logarithmic Deviation is a path-independent decomposable inequality measure

(Foster & Shneyerov, 2000). It is defined as: MLD(X) = 1N

∑N

1 ln µx

xiwhere X is a distri-

bution, N population size and µX is mean.

15

The first row of Table 4 shows the inequality decomposition where the variation

within types of students is suppressed by substituting each type’s accessibility

score with its arithmetic mean. By this method the inequality between the

types of students is computed as 0.01776 and represents the contribution of

socio-economic factors to total inequality (5% of total inequality). Whereas

in the second row the variation within provinces is eliminated by substituting

each province’s accessibility score with its arithmetic mean, in this approach

the inequality between provinces is computed as 0.34637 and the contribution

of socio-economic factors to total inequality is measures at 7%.

For the sake of a better understanding of the computed inequality,

take a female student with a poor family background (group 2) living in Mat-

era, in order for her to have as much access as a male student with the same

family origin (group 1) living in the same city, she has to travel 151 km to

the nearest city, Foggia (social distance). Moreover, in order for her to have

similar access to a female student with better family origin (group12) living in

Napoli, she has to travel 460 km to the nearest city (social+physical distance).

Therefore, the findings indicate that despite the expansion of HE supply in

the country, access to HE is strongly unequal due to the spatial distribution

of opportunities with additional disparity due to socio-economic factors at the

locations of origin.

Table 4 [About Here]

5 Conclusions

This paper provides empirical evidence for the dynamics of student mobility

in Italy and measures inequality in access to HE institutions with particular

attention to the socio-economic background of students. Using a spatial inter-

action model, the flows of students to universities are defined as interactions

between provinces in Italy. The results demonstrate that poor family back-

16

ground students are impervious to university-quality effects and university

quality becomes relevant only for students with better family backgrounds.

The model allowed for interactions between institutional characteristics and

distance to see how elasticity with respect to spatial distance varies given the

heterogeneity of the universities. The results indicates that private universi-

ties attract students and increase their willingness to travel longer distances.

Universities located in the central region attract more students than in the

south and the location in Sicily or Sardinia deters flows.

As far as distance is concerned, the values of these parameters are

highly significant and negative in sign, indicating a deterrence effect for each

group of students. In line with previous studies, δ was significant at the 0.01

level for all groups and positive in sign, capturing the benefit of residing and

studying in the same city. For the second step computed deterrence functions

and δs were imported into a Hansen-like accessibility index and accessibil-

ity scores of 110 Italian provinces to 77 Italian universities were computed.

The results show that socio-economic background matters especially for fe-

male student mobility. Finally, the share of aspatial factors in inequality of

access between types proved to be 5% with the first approach and 7% when

computed with the second approach.

This paper contributes to the accessibility literature by a multidis-

ciplinary approach providing a spatial accessibility measure for Italian HE

institutions with particular attention to socio-economic sources of inequality.

It also contributes to EOp literature by showing how spatial dimensions of

EOp could be incorporated into models that rely solely on spatial elements.

Furthermore, the investigation of 10 types provides a clear ranking. In other

words, through this application, this study empirically shows which socio-

economic group is better off and by how much. Finally, this study is the first

attempt to define parental location, clearly an exogenous factor for students,

as a circumstance.

17

The findings provide highly detailed information for policy implica-

tions. In order to increase accessibility three policy strategies can be adopted.

Firstly, an effective policy may target the types with lower potential acces-

sibility to assist them though loans, scholarships and grants. Secondly, the

geographical locations where accessibility is lower can be identified and ac-

cessibility can be increased by the reduction of geographic barriers for cities

such as Nuoro, Brindisi, Ragusa and Belluno where new universities and/or

places may be set up. Finally a combination of these two can be used. For

instance, the empirical evidence in this paper shows that female students with

disadvantaged family backgrounds located in southern Italy would benefit the

most from HE funding. More precisely the identification of inequality resulting

from gender and geography can be extracted from the findings as follows: for

a female student living in the south with low income parents both with a basic

education, on average the potential accessibility is 146.15% lower than a male

student living in the North with better family origin. These examples can be

extended to determine a variety of policy strategies.

18

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Table 1: Variables in analysis, data from ISTAT and MIUR

Variables Description

Residence Province of residence before enrollment (ISTAT)

Destination University Enrolled University 63 state and 14 private universities(ISTAT)

Distance Euclidean distance between city centroids and University addresses,

measured in QGIS based on coordinates

Sex 14,000 male and 17,400 female students graduated in 2007(ISTAT)

Parent’s Education The highest degree obtained by parents(ISTAT)

Two categories: at least one highly educated parent, both basic educated

“basic education” covers high school degree

high category at least bachelor’ s degree.

Financial Condition Occupation type of parents(ISTAT)

Three categories : both-high, one-high,both-low

High=Managers, Directors,High/Medium Qualification

Low=Office Worker, Lower-skilled workers

Sj Share of students who achieved highest scores (90-100)

from compulsory test before HE enrollment in the period 2002-2003 (MIUR)

Lj Proportion of limited places offered by universities to the total places (MIUR)

Ujl Institutional characteristics to be interacted with distance (MIUR)

Uj1 1 if private 0 otherwise

Uj2 1 if polytechnic 0 otherwise

Uj3 1 if south Uj4 1 if center Uj5 1 if island 0 otherwise

Types group1(both basic educated parents,male , low financial condition )

group2(both basic educated parents,female , low financial condition)

group3(both basic educated parents,male, medium financial condition)

group4(both basic educated parents, female, medium financial condition)

group5(both basic educated parents, male, high financial condition)

group6(both basic educated parents, female, high financial condition)

group7(at least one high educated parent, male, low financial condition )

group8(at least one high educated parent, female, low financial condition)

group9(at least one high educated parent, male, medium financial condition)

group10(at least one high educated parent, female, medium financial condition)

group11(at least one high educated parent, male, high financial condition)

group12(at least one high educated parent, female, high financial condition)

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Table 2: Results of Poisson Regression First 5 Groups

Groups (1) (2) (3) (4) (7)

Variables Basic-Male-Lower Class Basic-Female-Lower Class Basic-Male-Middle Class Basic-Female-Middle Class ≥ 1high-Male-Lower Class

Sj 0.455 0.697 0.556 -0.087 1.502***

(0.421) (0.373) (0.595) (0.504) (0.396)

Lj 0.158 0.303*** 0.214 0.277* 0.140

(0.098) (0.086) (0.133) (0.119) (0.123)

µ 0.249*** 0.293*** 0.261*** 0.300*** 0.307***

(0.389) ( 0.032) (0.056) (0.045) (0.036)

Distance -1.535*** -1.709*** -1.530*** -1.574*** -1.625***

(0.047) ( 0.045) (0.069) (0.060) (0.046)

Institutional Interactions

x Private Univ. 0.726*** 0.608*** 0.509*** 0.599*** 0.631***

(0.070) ( 0.058) (0.093) (0.067) (0.057)

x Polytechnic 0.003 0.240 0.105 0.276* 0.134*

(0.072) (0.088) (0.105) (0.105) (0.022)

x South 0.561*** 0.359*** 0.549*** 0.386*** 432***

(0.062) (0.059) (0.090) (0.076) (0.065)

x Center 0.527*** 0.545*** 0.418*** 0.495*** 0.467***

(0.060) (0.058) (0.088) (0.078) (0.058)

x Island -0.206*** -0.039 -0.130 -0.375* -0.283*

(0.155) (0.122) (0.201) (0.173) (0.162)

(Intercept) 6.528*** 7.258*** 5.442*** 6.304*** 6.790***

(0.227) (0.202) (0.392) (0.291) (0.221)

Observations 1,572 1,572 1,572 1,572 1,572

R2 0.88 0.89 0.87 0.86 0.86

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Education of Parents-Gender of Student-Financial Condition of Parents

25

Table 3: Results of Poisson Regression Last 5 Groups

Groups (8) (9) (10) (11) (12)

Variables ≥1high-Lower Class ≥1high-Male-Middle Class ≥ 1high-Female-Middle Class ≥ 1high-Male-Higher Class ≥1high-Female-Higher Class

Sj 1.745*** 2.291*** 1.444*** 2.006*** 1.947***

(0.337) (0.313) (0.287) (0.293) (0.260)

Lj 0.384*** 0.184*** 0.187*** 0.112** 0.348***

(0.065) (0.073) (0.072) (0.082) (0.081)

µ 0.337*** 0.287*** 0.281*** 0.305*** 0.273***

(0.032) ( 0.032) (0.029) (0.029) (0.027)

Distance -1.581*** -1.551*** -1.532*** -1.522*** -1.499***

(0.041) ( 0.040) (0.034) ( 0.035) (0.032)

Institutional Interactions

x Private Univ. 0.590*** 0.490*** 0.481*** 0.549*** 0.573***

(0.045) ( 0.043) (0.038) (0.037) (0.033)

x Polytechnic -0.538 0.070 0.706 0.072 0.042

(0.078) (0.051) (0.061) (0.047) (0.054)

x South 0.123* 0.436*** 0.198*** 0.320*** 0.255***

(0.636) (0.609) (0.054) (0.054) (0.049)

x Center 0.399*** 0.499*** 0.418*** 0.384*** 0.407***

(0.053) (0.516) (0.045) (0.045) ( 0.040)

x Island -0.150 0.816 -0.252* -0.490*** -0.331**

(0.125) (0.130) (0.111) (0.118) (0.103)

(Intercept) 6.654*** 7.113*** 7.084*** 6.851*** 6.939***

(0.194) (0.187) (0.172) (0.170) (0.155)

Observations 1,572 1,572 1,572 1,572 1,572

R2 0.85 0.87 0.89 0.86 0.88

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Education of Parents-Gender of Student-Financial Condition of Parents

26

Table 4: Decomposition of Inequality In Access (MLD measures)

Spatial Inequality Inequality due to socioeconomic background Total Inequality

Inequality in Access to HE (First Approach) 0.35444 0.01776 0.37220

Inequality in Access to HE (Second Approach) 0.34637 0.02583 0.37220

Percentage Contribution (First Approach) %95 %5 %100

Percentage Contribution (Second Approach) %93 %7 %100

27

Figure 1: First 5 Groups

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Figure 2: Last 5 Groups

29